Science Gone Crazy: COVID Model Generates False Conclusions

It was an Imperial College computer model that forecasted 500K deaths in the UK (and 2.5 million in the US) should policymakers pursue a “herd immunity” approach (a la Sweden), that influenced them to reverse course and go full lockdown instead. The model was produced by a team headed by Neil Ferguson, (who recently resigned his post advising the UK government when it surfaced that he was himself violating lockdown directives by breaking self-isolation for dalliances with a married woman).

The source code behind the model was to be made available to the public, and after numerous delays and excuses in doing so, has finally been posted to GitHub

A code review has been undertaken by an anonymous ex-Google software engineer here, who tells us the GitHub repository code has been heavily massaged by Microsoft engineers, and others, in an effort to whip the code into shape to safely expose it to the pubic. Alas, they seem to have failed and numerous flaws and bugs from the original software persist in the released version. Requests for the unedited version of the original code behind the model have gone unanswered.

The most worrisome outcome of the review is that the code produces “non-deterministic outputs”

Non-deterministic outputs. Due to bugs, the code can produce very different results given identical inputs. They routinely act as if this is unimportant.

This problem makes the code unusable for scientific purposes, given that a key part of the scientific method is the ability to replicate results. Without replication, the findings might not be real at all – as the field of psychology has been finding out to its cost. Even if their original code was released, it’s apparent that the same numbers as in Report 9 might not come out of it.

The documentation proffers the rationalization that iterations of the model should be run and then differing results averaged together to produce a resultant model. However, any decent piece of software, especially one that is creating a model, should produce the same result if it is fed the same initial data, or “seed”. This code doesn’t. …

 

Most of us are familiar with the computing adage, “Garbage In/Garbage Out” and the untrained reader may think that’s what being asserted in this code review. It isn’t. What’s being asserted is that output is garbage, regardless of the input. 

In this case, the output we’re experiencing as a result is a worldwide lockdown and shutdown of the global economy, and we don’t really know if this was necessary or not because we have no actual data (aside from Sweden) and severely flawed models.”